Best Data Mining Solution for Business Raise Forecast Accuracy with Powerful Data Mining Software Data mining is a set of methods intended to discover hidden patterns in large volumes of data. This relatively new data analysis method allows obtaining knowledge simply by extracting it from existing data and re-structuring it to human-understandable form. In particular, data mining is an effective and perspective way to manage customer relations, perform marketing studies and in other business applications. It is also commonly used in biology, genetics, medicine, power engineering, sports and game analysis. GMDH Shell is a data mining software developed for rapid detection of various data anomalies, information patterns, clusters and other dependencies in an input dataset. GMDH stands for Group Method of Data Handling; this mathematical method was developed by Ukrainian prof.

1. Data Mining Wiki

Data mining software uses advanced statistical methods (e.g. Algorithms, artificial intelligence, machine learning) to identify sequential patterns and correlations within large volumes of pre-collected business data, and transform those into meaningful, actionable data. Data Mining will count the number Open the text file (or paste the text to be mined into the window) and type up to 28 strings (case-sensitive) to be searched. Data Mining will count the number. Data mining, also known as knowledge discovery from databases, is a process of mining and analysing enormous amounts of data and extracting information from it. Data mining can quickly answer business questions that would have otherwise consumed a lot of time. Data Mining will count the number Open the text file (or paste the text to be mined into the window) and type up to 28 strings (case-sensitive) to be searched. Data Mining will count the number. Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

Data Mining Wiki

Ivakhnenko in 1968. Since then, the method was repeatedly improved, and the GMDH Shell team has developed much more advanced version of this algorithm. The main idea behind the method is applying gradually complicating models for data analysis and trend prediction. Every consecutive model is built using one of the known approximation methods – linear, polynomial, Gaussian, neural networks, combinatorial search and others). The model is tested for predictive strength and is verified against historical data. If previously observed data do not fit the model it gets complicated and the process repeats until a model describing historical data best of all is found. While many data mining software works using the classic combinatorial GMDH approach, the advanced algorithm carries out the same tasks much faster.